A privacy-preserving collaborative filtering scheme with two-way communication
Sheng Zhang, James Ford, Fillia S. Makedon · 2006
An important security concern with traditional recommendation systems is that users disclose information that may compromise their individual privacy when providing ratings. A randomization approach has been proposed to disguise user ratings while still producing accurate recommendations. However, recent research has suggested that a significant amount of original private information can be derived from perturbed data in a randomization scheme. We suggest that a main limitation of the existing randomization approach is that perturbation is item-invariant--each item has a same perturbation variance. Based on this observation, we introduce a two-way communication privacypreserving scheme in which users perturb their ratings for each item based on the server's guidance instead of using an item-invariant perturbation. Compared to the existing randomization approach, our new scheme can help users disclose much less private information at the same recommendation accuracy level.